Abstract
Objectives To design a frailty index (FI) and evaluate three methods to handle missing data. Furthermore, we evaluated its construct (i.e., skewed distribution, correlation with age and sub-maximum score) and criterion validity (based on mortality risk). Study design We included 11,539 participants (45± years) from a population-based cohort in the Netherlands. Frailty was measured with a FI, which we constructed based on the accumulation of 45 health-related variables, related to mood, cognition, functional status, diseases and conditions, biomarkers, and nutritional status. A total FI-score was calculated by averaging the scores of the deficits, resulting in a score between 0 and 1, with higher scores indicating increasing frailty. Mean imputation, single- and multiple imputation were applied. Main outcome measure Mortality data were obtained by notification from the municipal administration. Median follow-up time was 9.5 years, during which 3902 (34%) participants died. Results The median FI for the full population was 0.16 (IQR = 0.11–0.23). The distribution of the FI was slightly right-skewed, the absolute maximum score was 0.78 and there was a strong correlation with age (Pearson correlation = 0.52;95%CI = 0.51–0.54). The adjusted HR per unit increase in FI-score on mortality was 1.05 (95%CI = 1.05–1.06). Multiple imputation seemed to provide more robust results than mean imputation. Conclusion Based on our results we advise to the use of at least 30 deficits from different health domains to construct a FI if data are not imputed. Future research should use the continuous nature of the FI to monitor trajectories in frailty and find preventive strategies.
Original language | English |
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Pages (from-to) | 14-20 |
Number of pages | 7 |
Journal | Maturitas |
Volume | 97 |
DOIs | |
Publication status | Published - 1 Mar 2017 |
Externally published | Yes |
Keywords
- Construct validity
- Criterion validity
- Frailty index
- Missing data
- Mortality